Machine Learning /MLOps Engineer

ABOUT THE ROLE

The Machine Learning Engineer will join a newly formed team focused on MLOps and ML Engineering within the data science team. This role is crucial for uplifting the existing ML infrastructure into a robust, scalable, and production-ready system, specifically supporting the increasing needs of the company. The engineer will design, build, and maintain the infrastructure, tools, and processes required for continuous integration, continuous delivery, and continuous training (CI/CD/CT) of machine learning models. This is a highly technical role that will interface with data scientists, data engineers, data architects and software engineers to ensure the reliability and performance of ML systems at scale.

YOU WILL BE RESPONSIBLE FOR:

● Designing, implementing, and maintaining scalable MLOps pipelines for model training, testing, deployment, and monitoring.

● Building and managing the cloud infrastructure to host and serve real-time and batch machine learning models.

● Establishing robust model monitoring (drift, performance, data quality) and alerting systems to ensure operational health of ML services.

● Assisting and collaborating with Data Scientists to ensure production readiness of models, focusing on code optimization for low latency and performance.

● Developing high-quality, reusable MLOps tooling and best practices to drive efficiency across the entire ML lifecycle.

● Driving automation initiatives to minimize manual intervention in model deployment and management.

● Troubleshooting production issues, performing root cause analysis, and implementing preventative measures for ML systems.

OUR SUCCESSFUL CANDIDATE WILL HAVE THE FOLLOWING:

Essentials

● Mid-senior level experience in a Machine Learning Engineer, MLOps Engineer, or similar role.

● Strong proficiency in Python for data and ML workflows and infrastructure automation tools (e.g. Terraform) for scalable infrastructure provisioning.

● Ext